P719 Identification of differentially expressed genes between AIEC and non-AIEC clinical isolates during in vitro cell infection

Bonet Rossinyol, Q.(1);Camprubí-Font, C.(1);Lopez-Siles, M.(1);Martinez-Medina, M.(1);

(1)Universitat de Girona, Biology, Girona, Spain; Microbiology of Intestinal Diseases Research Group


Adherent invasive Escherichia coli (AIEC) have been related to Crohn’s disease. Phenotypic assays of adhesion and invasion of intestinal epithelial cells and intra-macrophage replication are used to identify AIEC. No molecular tools are still available for AIEC identification. We hypothesise that differential gene expression may drive to the AIEC phenotype. To test this hypothesis we sequenced the genes expressed by AIEC and non-AIEC strains during infection, and we investigated differential expressed genes (DEGs) suitability as molecular markers.


Comparative transcriptomics was performed between two AIEC/non-AIEC strain pairs, belonging to the phylogroups B1 and D, during Intestine-407 cells infection. Each strain pair displayed identical pulsotypes and similar genomes. Supernatant fractions of the infected cell cultures (SN) and eukaryotic cells containing adhered and/or intracellular bacteria (A/I) were considered separately. DEGs obtained were quantified by RT-qPCR in the same samples to confirm the results, and in a strain collection of 13 AIEC and 23 non-AIEC to investigate their suitability as molecular markers. Binary logistical regression was performed to identify DEGs whose quantification could be used as AIEC biomarker. 


Comparative transcriptomics revealed 67 DEGs between the two phenotypes in the strain pairs (19 over-expressed in AIEC, and 48 under-expressed), 51 of which (82.26%) were corroborated by RT-qPCR.  When explored in the whole strain collection, 29 DEGs were found to be differentially expressed between AIEC and non-AIEC phenotypes (p < 0.042), and 42 genes between SN and A/I fractions (p < 0.049). Notably, six DEGs detected in the strain collection were implicated in a metabolic pathway that could be involved in acid resistance, and eight in the synthesis of two virulence determinants. Finally, binary logistical regression revealed three DEGs able to predict the AIEC phenotype with an accuracy of ≥ 85%.


No previous comparative transcriptomic studies have been performed using AIEC-infected cell cultures. Our research revealed three genes, whose quantification presents higher sensitivities and specificities than other molecular markers previously suggested. Moreover, we have identified functionally related genes that can be involved in AIEC pathogenicity. These results open the door to further research to confirm the suitability of these genes as AIEC biomarkers and to investigate new therapeutic targets against AIEC colonisation.